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Image filtering

Image filtering. Hybrid Images, Oliva et al., http://cvcl.mit.edu/hybridimage.htm. Image filtering. Hybrid Images, Oliva et al., http://cvcl.mit.edu/hybridimage.htm. Reading. Szeliski, Chapter 3.1-3.2. What is an image?. Images as functions.

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Image filtering

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  1. Image filtering Hybrid Images, Oliva et al., http://cvcl.mit.edu/hybridimage.htm

  2. Image filtering Hybrid Images, Oliva et al., http://cvcl.mit.edu/hybridimage.htm

  3. Reading • Szeliski, Chapter 3.1-3.2

  4. What is an image?

  5. Images as functions • We can think of an image as a function, f, from R2 to R: • f( x, y ) gives the intensity at position ( x, y ) • Realistically, we expect the image only to be defined over a rectangle, with a finite range: • f: [a,b]x[c,d]  [0,1] • A color image is just three functions pasted together. We can write this as a “vector-valued” function:

  6. Images as functions

  7. What is a digital image? • We usually work with digital (discrete)images: • Sample the 2D space on a regular grid • Quantize each sample (round to nearest integer) • If our samples are D apart, we can write this as: • f[i ,j] = Quantize{ f(iD, jD) } • The image can now be represented as a matrix of integer values

  8. Filtering noise • How can we “smooth” away noise in an image?

  9. Mean filtering

  10. Mean filtering

  11. We can generalize this idea by allowing different weights for different neighboring pixels: • This is called a cross-correlation operation and written: • H is called the “filter,” “kernel,” or “mask.” • The above allows negative filter indices. When you implement need to use: H[u+k,v+k] instead of H[u,v] Cross-correlation filtering • Let’s write this down as an equation. Assume the averaging window is (2k+1)x(2k+1):

  12. Mean kernel • What’s the kernel for a 3x3 mean filter?

  13. Mean vs. Gaussian filtering

  14. Gaussian filtering • A Gaussian kernel gives less weight to pixels further from the center of the window • This kernel is an approximation of a Gaussian function: • What happens if you increase  ?Photoshop demo

  15. Image gradient • How can we differentiate a digital image F[x,y]? • Option 1: reconstruct a continuous image, f, then take gradient • Option 2: take discrete derivative (finite difference) • How would you implement this as a cross-correlation?

  16. The gradient direction is given by: • how does this relate to the direction of the edge? • The edge strength is given by the gradient magnitude Image gradient • It points in the direction of most rapid change in intensity

  17. original smoothed (5x5 Gaussian) Why doesthis work? smoothed – original (scaled by 4, offset +128)

  18. Gaussian delta function Laplacian of Gaussian (2nd derivative operator)

  19. Convolution • A convolution operation is a cross-correlation where the filter is flipped both horizontally and vertically before being applied to the image: • It is written: • Suppose H is a Gaussian or mean kernel. How does convolution differ from cross-correlation? • Suppose F is an impulse function (previous slide) What will G look like?

  20. Continuous Filters • We can also apply filters to continuous images. • In the case of cross correlation: • In the case of convolution: • Note that the image and filter are infinite.

  21. More on filters… • Cross-correlation/convolution is useful for, e.g., • Blurring • Sharpening • Edge Detection • Interpolation • Convolution has a number of nice properties • Commutative, associative • Convolution corresponds to product in the Fourier domain • More sophisticated filtering techniques can often yield superior results for these and other tasks: • Polynomial (e.g., bicubic) filters • Steerable filters • Median filters • Bilateral Filters • … • (see text, web for more details on these)

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